Deep neural networks are susceptible to adversarial perturbations of their input data that can cause a sample to be incorrectly classified.These perturbations contain small variations in the pixel space that cannot be detected by a human but can change the output of a classifier.Reliably detecting attacks in a given set of inputs is of high practical relevance due to the vulnerability of neural networks to adversarial examples.
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